[rgb]0,0,0We find that the existing methods have not considered training a task-specific feature encoder efficiently, which offers discriminative representations for events under surveillance cameras.
[rgb]0,0, 0To overcome this problem for WS-VAD, we develop a two-stage self-training procedure (Figure REF ) that aims to train a task-specific feature encoder with only video-level weak labels.
In particular, we propose a Multiple Instance Self-Training framework (MIST) that consists of a multiple instance pseudo label generator and a self-guided attention boosted feature encoder \(E_{SGA}\) .
1) MIL-pseudo label generator.
The MIL framework is well verified in weakly supervised learning. MIL-based methods can generate pseudo labels more accurately than those simply assigning video-level labels to each clip [1]}. Moreover, we adopt a sparse continuous sampling strategy that can [rgb]0.,0.,0force the network to pay more attention to context around the most anomalous part. 
2) Self-guided attention boosted feature encoder. Anomalous events in surveillance videos may occur in any place and with any size [2]}, while in commonly used action recognition videos, the action usually appears with large motion
[3]}, [4]}. Therefore, we utilize the proposed self-guided attention module in our proposed feature encoder to emphasize the anomalous regions without any external annotation [2]} but [rgb]0,0, 0clip-level annotations of normal videos and clip-level pseudo labels of anomalous videos. For our WS-VAD modelling, we introduce a deep MIL ranking loss to effectively train the multiple instance pseudo label generator.
[rgb]0,0,0In particular, for deep MIL ranking loss, we adopt a sparse-continuous sampling strategy to focus more on the context around the anomalous instance.
